Volume XI Issue VIII

In this new Era the growing Vehicle population in all developing and developed country calls for a major improvement and innovation in the existing Traffic control systems. This goal of this paper is to model a system that uses an efficient traffic light system to control autonomous vehicles at intersections and also gives priority to emergency vehicles. The optimized traffic control system for vehicles are intelligent, the traffic light agents are reinforced using Q-Learning. The most widely used automated system uses a simple time based system which working on a time interval basis which is now inefficient for random and non-uniform Traffic. The system assigns the emergency vehicles a separate lane in the network. The system will detect and identify vehicles that exceed the maximum authorized speed limit on selected carriageways, and is capable to grant priority to selected vehicles (e.g. Public Transport (PT), Emergency Vehicles) at an intersection controlled by some form of traffic signals. The system helps to enhance traffic control system and management and its technology senses presence of Traffic near any junction and then able to route the Traffic based on Traffic availability. This system does not require any system in vehicles so can be implemented in any Traffic system quite easily with less time and less expensive also

This study examined the efficiency, profitability and constraints that confront lowland rice farmers in Kogi State, Nigeria. A multi-stage random sampling procedure was used to collect primary data for the study. The first stage involved a purposive selection of two local government areas based on predominance of lowland rice production. The second stage involved a random selection of 8 villages. The final stage involved the random selection of one hundred and forty-nine (149) lowland rice-producing households from the villages selected. Data were analysed using descriptive statistics, Cobb-Douglas stochastic frontier production function and Gross Margin Analysis. The stochastic frontier production function was estimated for technical, allocative and economic efficiency. The results revealed technical efficiency range of 0.19 to 0.95. The mean estimate was 0.83. The efficiency distribution had shown that, about 83 percent of the lowland rice farmers attained between 0.61 and 1.00 efficiency levels, while 17% of the farmers operate at less than 0.6 efficiency levels. The result also indicates that, the average lowland rice farmer would realize about 35.79 percent in cost savings, if he or she was to attain the level of the most efficient farmer in the sample. The result further shows that there are allowances for the farmers to improve their efficiency by about 17 percent. The allocative efficiency estimates revealed that, the allocative distribution ranged from 0.21 to 1.00; the mean allocative efficiency was 0.81. The result indicates that average lowland rice farmer in the State would enjoy cost saving of about 20 percent if he or she attains the level of the most efficient farmer among the respondents. The most allocatively inefficient farmer will have an efficiency gain of 84 percent in certified rice seed production if he or she is to attain the efficiency level of most allocatively efficient farmer in the state. The study therefore suggests intensive efforts at expanding the present scope of lowland rice farming, given the estimated technical efficiency in the production system.

Jute knitted fabric was produced in knitting lab and was treated with natural and synthetic dust resistance compounds of perchloroethylene, wax, and mixers of vinegar and olive oil at different temperatures with contact of dust to develop anti-dust characteristics on jute knitted fabrics. It was found that at 80oC temperature, perchloro ethylene developed dust resistant property on jute knitted fabric whereas at 60oC, wax treatment improved a dust resistant property and in natural treatment at room temperature, spray of vinegar+olive oil improved dust resistant property as well

The swift rise of the average global temperature, as a result of the increasing rates of greenhouse gas emission, is an issue that most countries around the world are pooling in large amounts of resources to solve. In addition, countries all over the world, especially those south of the Sahara Desert have load shedding problem because electricity demand grows faster than the rate at which power plant capacities are increased. In order to ensure high power system stability and reliability with the added benefit of low running cost, while curbing climate change, some of these countries including Ghana are developing their power system around renewable energy resources such as solar. Countries in the tropical zone, specifically, African States have high solar energy potential across the entire region all over the year. However, the usage of solar energy for electricity generation is very low. One way to improve the efficiency of solar PV panels is to increase the energy density of the equipment by combining solar photovoltaic and solar thermal system in a technology known as Photovoltaic Solar Thermal (PV/T) Collector. The study focused on observing the performance of the PV/T system in terms of electrical energy efficiency, and total energy conversion efficiency using MATLAB software. This paper used climatic condition data for Navrongo Solar Power Plant in Ghana as a case study information for the work. The research established that, photovoltaic thermal system has a higher electrical efficiency than an ordinary PV panel (solar photovoltaic system) irrespective of the prevailing weather conditions. However, the thermal energy extracted contributed less than 1% to the overall energy conversation efficiency as a result of a very low flow rate of the working fluid (water).